• Energy Research

Abstract China has recently changed its regulation for producing energy from photovoltaic solar panels in order to encourage the use of the solar resource. This new regulation started with offering subsidies at a national level and this was later followed by local subsidies in addition to the national one. Being a large country, China has regions with good solar exposure and others with poor exposure. Each region has a different electricity price and the energy is purchased based on the Grid Coal Power electricity price that also varies throughout the country. In this work we analyze the economic profitability of different regions considering the solar radiation levels, savings in self-consumption, cash flows from injecting power into the grid and local prices for installations to show that the best return is obtained in the places with better solar radiation or where the electricity price is higher. The regional Feed-In tariffs help to compensate for lower radiation levels but do not make these regions very attractive from an investment perspective.

AbstractMachine learning techniques (MLTs) can create accurate predictions of solar outputs that are used in photovoltaic system performance analysis. The issue with MLT application is the requirement for large amounts of historical data for training the prediction models that is not always available. Since the photovoltaic system behaviour is non‐linear due to the unpredictable nature of the weather conditions throughout the year, MLT training requires annual historical data to create the prediction model. The photovoltaic system production meters only store up to 3 months of historical power values. This information served as motivation to research different types of MLTs, in search of one that would accurately predict the daily solar energy values of a photovoltaic system based on the available 3‐month historical data. The aim of this work is to implement a photovoltaic system performance analyser that estimates daily solar alternating current energy outputs for any rooftop photovoltaic system, based on daily solar irradiation values, without being influenced by the seasons of the year nor the photovoltaic system installation location. Therefore, 5 MLTs were studied and compared, which include the regression tree, artificial neural network, multigene genetic programming, Gaussian process, and the support vector machine for regression. Results show that the regression tree MLT provides acceptable results to be used in all locations and all seasons of the year, while the support vector machine for regression is best for spring and summer training dataset months, and the Gaussian process is best for the autumn and winter training dataset months.

Abstract Over the last few years, feed-in tariff for PV Systems in many countries have been significantly reduced and new regulations are being placed to promote the development of renewable energy and support self-consumption by paying lower grid-injected electricity tariffs compared to regular electricity price. The purpose of this paper is to analyze a representative set of countries, including Australia, Brazil, China, Germany, India, Iran, Italy, Japan, Portugal, South Africa, Spain, United Kingdom, and the United States of America, to identify the ones with the best investment opportunities considering the new regulations. Two case studies are included in this paper with different sizes of solar photovoltaic systems (1 kW and 5 kW). Each case study includes four different consumption scenarios ranging from 100% self-consumption to 30%. Overall, the results show that the most profit can be made in Australia, Germany, and Italy. In these countries, it is possible to quadruple the investment during the 25-year period with a 5 kW PV system which is roughly 13% higher than most European countries. Furthermore, this study explores the current policies and conditions of small-scale solar PV industry in the selected countries, providing enormous benefit to various entities namely policy makers, investors, and researchers who are working under the solar energy domain.